What is the difference between MDX and DAX

MDX (Multidimensional Expressions) and DAX (Data Analysis Expressions) are both query and calculation languages used in Microsoft's BI (Business Intelligence) stack, but they serve different purposes and are associated with different data modeling technologies. Here's a breakdown of the key differences between MDX and DAX:

1. Associated Technologies:

   • MDX is primarily associated with OLAP (Online Analytical Processing) technologies, such as Microsoft SQL Server Analysis Services (SSAS) Multidimensional and other multidimensional databases.
   • DAX, on the other hand, is associated with tabular modeling technologies, such as Microsoft Power BI, SQL Server Analysis Services Tabular, and Excel Power Pivot.

2. Data Model:

   • MDX operates on multidimensional data models, which are typically represented as cubes with dimensions, hierarchies, and measures. These models are common in traditional OLAP systems.
   • DAX operates on tabular data models, which are relational and columnar in nature. Tabular models consist of tables and relationships between tables, similar to a traditional relational database.

3. Query Language:

   • MDX is primarily a query language designed for querying and manipulating multidimensional data. It allows users to retrieve data from OLAP cubes, perform calculations, and navigate hierarchies.
   • DAX is a formula language designed for creating calculated columns, calculated tables, and measures in tabular models. It's used for defining calculations and aggregations within tables.

4. Syntax:

   • MDX syntax is similar to SQL (Structured Query Language) but is specialized for multidimensional data. It includes constructs for querying dimensions, hierarchies, sets, and performing multidimensional calculations.
   • DAX syntax is similar to Excel formulas and includes functions for aggregating, filtering, and manipulating data within tables. DAX expressions are often written in a formula bar or as part of calculated columns and measures.

5. Usage:

   • MDX is commonly used in scenarios where complex multidimensional analysis is required, such as financial reporting, budgeting, and forecasting in OLAP cubes.
   • DAX is widely used in self-service BI and data modeling scenarios, particularly with Power BI, where users create reports, dashboards, and visualizations based on tabular models.

6. Performance:

   • MDX queries tend to be optimized for querying multidimensional data efficiently, taking advantage of features like pre-aggregated measures and hierarchies.
   • DAX expressions are optimized for performance in tabular models, with features like query folding and in-memory columnar storage.

In summary, MDX and DAX are both powerful languages for working with data in Microsoft's BI ecosystem, but they cater to different data modeling technologies and serve different analytical needs. MDX is associated with OLAP and multidimensional models, while DAX is associated with tabular models and self-service BI.